Week 1By Rachel Huang and Jackson Maris

University of North Carolina Wilmington Statistical Data Mining and Machine Learning

May 25, 2018

OutlineProject 1:

- Numerical and graphical summaries of age and gender using the MORPH-II partial

dataset

Project 2:

- Analyzing the relation between age, gender and race using the entire MORPH-II

dataset

Project 3:

- Creating regression models and classifying data from the MORPH-II partial dataset

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Project 1Process:

- Two vectors were created for age and gender.

- The 7th character from the end of the filename, either “M” or “F”, indicates

gender.

- The 6th and 5th characters from the end of the filename indicate the age.

- The remaining 2,568 columns are the corresponding Bio-Inspired Features (BIF)

for each person.

Part of a row in the dataset.3

Project 1 Age Analysis

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Project 1 Age Graphs

Put graphs here

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Project 1 Gender Analysis

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Project 2Process:

- Analyzed the difference between a clean and dirty data set.

- Looked at relationship between:

- Gender and race

- Gender and additional arrests

- Combining the partial and full Morph-II data sets

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Morph_2008_nonCommercial.csv

# of males: 11,459

# of females: 2,159

# distinct number of subjects: 13, 618

Black White Hispanic Asian Other Total

Male 8,838 2,070 517 49 15 11,489

Female 1,494 634 30 6 5 2,169

Total 10,332 2,704 547 55 20 13,6589

morphII_cleaned_v2.csv

# of males; 11,458

# of females: 2,159

# of subjects: 13,617

Black White Hispanic Asian Other Total

Male 8,829 2,056 507 47 19 11,458

Female 1,491 628 28 4 8 2,159

Total 10,320 2,684 535 51 27 13,617

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Additional images 1 2 3 4 5+ Total

Male 2350 3606 1975 1135 2020 11086

Female 478 0 352 172 360 1362

Total 2828 3606 2372 1307 2380 12448

1 2 3 4 5+ Total

Male 2350 3606 1975 1135 2020 11086

Female 478 712 352 172 360 2074

Total 2828 4318 2327 1307 2380 1344011

Decade-of-Life <20 20-29 30-39 40-48 50+ Total

Male 1966 3068 2556 2071 755 10416

Female 294 541 590 433 99 1957

Total 2260 3609 3146 2504 854 12373

<20 20-29 30-39 40-49 50+ Total

Male 1968 3943 3370 2794 990 13065

Female 294 672 754 570 140 2430

Total 2262 4615 4124 3364 1130 15495

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Combining MORPH-II datasets

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Project 3Process:

- The first 20 BIF points were used to calculate the linear,

quadratic and polynomial regressions.

- Logistic regression used the entire BIF data set to build models.

Unfinished:

- LDA, QDA, and KNN

- Splitting the data into training and testing data

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Linear, Quadratic and Polynomial Regression

Adjust R^2:

- Linear: .0446

- Quadratic: .04403

- Polynomial: .04738

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Logistic RegressionAccuracy: .931

Error: .069

Sensitivity: .77

Specificity: .96

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Questions?

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